Selection of optimal texture algorithms for evaluating degradation of carpets through experimental design
نویسندگان
چکیده
Optimal texture analysis algorithms for describing degradation of carpets are identified. Experimental design is applied to select from a set of texture analysis algorithms those optimal for identifying texture changes due to degradation of carpets. The degree of wear of a degraded carpet is quantified by comparing its texture to the original texture. The set of texture algorithms is applied on intensity images obtained from the American and the European standards. The performance of the texture algorithms is evaluated using measures that quantify characteristics in the relationship between the metrics and the changes in texture. The statistical analysis of the experimental results shows that the local binary patterns algorithm is optimal in >50% of the cases, for describing degradation of the carpets. Other texture algorithms that optimally characterize the degradation of carpets include the use of the power spectrum, Wigner distribution, and average co-occurrence matrix algorithms. © 2013 SPIE and IS&T [DOI: 10.1117/1.JEI.22.3.033020]
منابع مشابه
Online Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملApplication of experimental design approach for optimization of the photocatalytic degradation of humic substances in aqueous solution using immobilized ZnO nanoparticles
Degradation of humic substances in water is important due to its adverse effects on the environment and human health. The aim of this study was modeling and investigating the degradation of humic substances in water using immobilized ZnO as a catalyst. ZnO nanoparticles were synthesized through simple coprecipitation (CPT) method and immobilized on glass plates. The immobilized ZnO nanocatalyst...
متن کاملUsing Neural Networks and Genetic Algorithms for Modelling and Multi-objective Optimal Heat Exchange through a Tube Bank
In this study, by using a multi-objective optimization technique, the optimal design points of forced convective heat transfer in tubular arrangements were predicted upon the size, pitch and geometric configurations of a tube bank. In this way, the main concern of the study is focused on calculating the most favorable geometric characters which may gain to a maximum heat exchange as well as a m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- J. Electronic Imaging
دوره 22 شماره
صفحات -
تاریخ انتشار 2013